Children commonly suffer from structural abnormalities of the upper airway that result in insufficient respiration and problems ranging from simple obstructive sleep apnea to imminent, life-threatening airway obstruction. In many cases dynamic airway collapse significantly contributes to airway obstruction, but methods for quantitative assessment of the dynamic airway are lacking. Quantitative imaging and computational modeling of the dynamic airway could prove very beneficial not only for the diagnosis of airway obstruction but also for aiding in medical and surgical decision-making, and could be utilized for predictive modeling and subsequent treatment planning. Current clinical methods for quantitative airway imaging, including MRI and CT, suffer from several limitations. Infants and young children may need to be sedated or anesthetized for long scan times, which can be quite hazardous for patients with airway obstruction. In the case of CT, there are risks associated with ionizing radiation exposure. These methods also do not readily offer real-time imaging to study airway dynamics. Airway endoscopy (laryngoscopy and bronchoscopy) is the gold standard for the evaluation of airway obstruction and is widely used to provide qualitative diagnostic information. We propose to address the need for quantitative, real-time, dynamic upper airway imaging by developing a technology based upon anatomic Optical Coherence Tomography (aOCT) delivered via standard bronchoscopes. The data acquired from this new technology will be manipulatable into 3D computational models of the airway upon which computational fluid dynamic (CFD) modeling will be performed. Our first Specific Aim will be to validate aOCT as a functionally equivalent substitute for CT for creating 3D virtual airway geometries for CFD. Our hypothesis is that the upper airway luminal geometries obtained by bronchoscopic aOCT can predict equivalent air flow resistance as that obtained by CT in cadaveric pigs. As an exploratory aspect of this Aim, we will perform imaging of adult human cadaveric lungs to evaluate the capability for quantitative imaging beyond the carina into the main stem, segmental, and smaller bronchi. Our second Specific Aim will be to incorporate technological advances into the aOCT system to enable dynamic, real-time (3+1) dimensional imaging to capture phases of the respiratory cycle in an in vivo pig model. Our third Specific Aim will be to perform elastography of the airway wall by collecting simultaneous in situ pressure and aOCT imaging data of pigs in vivo to during spontaneous respiration and while under variable positive airway pressure. This data will inform a dynamic flexible airway model for CFD computations that model the fluid-structure interaction (FSI). These pre-clinical steps of validation, technological advancement, and association with physiologic parameters of obstructive airway diseases will position the aOCT technology for rapid translation to clinical airway imaging.